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PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

Nature Methods volume 18, pages 520–527 (2021)Cite this article

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Abstract

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

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Fig. 1: An overview of the PCprophet framework.
Fig. 2: Benchmarking PCprophet against CCprofiler and EPIC for protein complex profiling and prediction.
Fig. 3: Evaluation of de novo protein complex prediction using PCprophet.
Fig. 4: Differential analysis of protein complexes across the cell-cycle states tested.

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Data availability

The annotated training dataset (including the calculated features and manual annotation) for the core RF model of PCprophet, and the GO term files (AmiGO2 database; http://amigo.geneontology.org/amigo) are available at https://github.com/anfoss/PCprophet and on Zenodo: https://doi.org/10.5281/zenodo.4574600). The information of the original raw dataset for training PCprophet and the other two datasets used for benchmarking and differential analysis have been demonstrated in Supplementary Table 2. The protein interaction and complex data were downloaded directly from the STRING (https://string-db.org/cgi/download), CORUM (core complexes; available at http://mips.helmholtz-muenchen.de/corum/#download), BioGRID (https://downloads.thebiogrid.org/BioGRID) and BioPlex (https://bioplex.hms.harvard.edu/) databases. All protein structures referred in this study have been provided with their PDB (https://www.rcsb.org/) IDs (4GQB, 6J2C and 6J2Q). The proteomics DDA and PRM raw files are available in PRIDE59 (https://www.ebi.ac.uk/pride/) under the identifier PXD022175. Source Data for Figs. 2–4 are available with this paper. Source data for Supplementary Figs. 1–5 and 11 are available at https://github.com/anfoss/PCprophet. The data that support the findings of this study are also available from the corresponding author upon request.

Code availability

PCprophet is open-access and freely available for academic purposes at https://github.com/anfoss/PCprophet under the MIT License. For the packages used to analyze MS data in this study, please refer to the Supplementary Notes for more details.

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Acknowledgements

This work was supported financially in part by the Swiss National Science Foundation (grant No. 3100A0-688 107679 to R.A.) and the European Research Council (ERC-20140AdG 670821 to R.A.). C.L. is currently supported by a National Health and Medicine Research Council (NHMRC) of Australia CJ Martin Early Career Research Fellowship (1143366). F.U., M.G. and F.F. were supported by the Innovative Medicines Initiative project ULTRA-DD (FP07/2007-2013, grant no. 115766). F.W. and B.W. were supported by the ETH strategic focus area ‘Personalized Health and Related Technologies (PHRT)’. M.G. acknowledges support from the EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN, grant no. 875510). I.B. acknowledges funding support from the Swiss National Science Foundation (31003A_166435). A.W.P. is supported by an NHMRC Principal Research Fellowship (1137739). We thank E. Milani for providing the vector pcDNA5/FRT/TO/SH/GW containing the ORF for GFP. We thank N. de Souza from ETH Zürich for her critical comments on this study.

Author information

Author notes

  1. These authors contributed equally: Andrea Fossati, Chen Li.

Authors and Affiliations

  1. Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland

    Andrea Fossati, Chen Li, Federico Uliana, Fabian Frommelt, Moritz Heusel, Isabell Bludau, Peng Xue, Matthias Gstaiger & Ruedi Aebersold

  2. Department of Biology, Institute of Molecular Health Sciences, ETH Zürich, Zürich, Switzerland

    Andrea Fossati

  3. Quantitative Biosciences Institute (QBI) and Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA

    Andrea Fossati

  4. J. David Gladstone Institutes, San Francisco, CA, USA

    Andrea Fossati

  5. Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

    Chen Li, Jiangning Song & Anthony W. Purcell

  6. Department of Health Sciences and Technology, Institute of Translational Medicine, ETH Zürich, Zürich, Switzerland

    Fabian Wendt & Bernd Wollscheid

  7. Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria

    Peter Sykacek

  8. Division of Infection Medicine (BMC), Department of Clinical Sciences, Lund University, Lund, Sweden

    Moritz Heusel

  9. Department for BioMedical Research, University of Bern, Bern, Switzerland

    Mahmoud Hallal

  10. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Isabell Bludau

  11. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Tümay Capraz

  12. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Peng Xue

  13. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Bernd Wollscheid

  14. Faculty of Science, University of Zürich, Zürich, Switzerland

    Ruedi Aebersold

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  1. Andrea Fossati
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Contributions

R.A., A.F., M. Heusel, C.L. and M.G. conceived and designed the project. A.F. and C.L. designed, developed and implemented PCprophet, and conducted data analysis, machine-learning prediction and benchmarking experiments with other existing methods. F.U. and F.W. performed experimental validation of the predicted complexes and interactions in the study. P.S. designed and implemented the differential analysis module of PCprophet for protein complexes. M. Heusel provided data and initial analysis. M. Heusel, F.F. and F.U. contributed to data annotation and provided critical feedback and comments on the biological aspects. M. Hallal contributed to EPIC performance comparison and reproducibility test of the PCprophet performance. I.B. assisted with benchmarking experiments with CCprofiler and provided useful insights. T.C., P.X., J.S., B.W. and A.W.P. provided critical and insightful comments during the development of PCprophet. C.L., A.F., M.G. and R.A. drafted the manuscript, which has been revised and approved by all authors.

Corresponding authors

Correspondence to Chen Li, Matthias Gstaiger or Ruedi Aebersold.

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The authors declare no competing interests.

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Peer review information Nature Methods thanks Stefan Tenzer and the other, anonymous, reviewers, for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Fossati, A., Li, C., Uliana, F. et al. PCprophet: a framework for protein complex prediction and differential analysis using proteomic data. Nat Methods 18, 520–527 (2021). https://doi.org/10.1038/s41592-021-01107-5

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